WO2024040292A1 - Procédé amélioré pour classification de semence comestible et dispositif de balayage s'y rapportant - Google Patents

Procédé amélioré pour classification de semence comestible et dispositif de balayage s'y rapportant Download PDF

Info

Publication number
WO2024040292A1
WO2024040292A1 PCT/AU2023/050805 AU2023050805W WO2024040292A1 WO 2024040292 A1 WO2024040292 A1 WO 2024040292A1 AU 2023050805 W AU2023050805 W AU 2023050805W WO 2024040292 A1 WO2024040292 A1 WO 2024040292A1
Authority
WO
WIPO (PCT)
Prior art keywords
seed
aflatoxin
wavelengths
spectral
reflectance
Prior art date
Application number
PCT/AU2023/050805
Other languages
English (en)
Inventor
Tom Martin
Wilmer Ariza
Gayatri Mishra
Original Assignee
Surenut Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2022902420A external-priority patent/AU2022902420A0/en
Application filed by Surenut Pty Ltd filed Critical Surenut Pty Ltd
Publication of WO2024040292A1 publication Critical patent/WO2024040292A1/fr

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3425—Sorting according to other particular properties according to optical properties, e.g. colour of granular material, e.g. ore particles, grain
    • B07C5/3427—Sorting according to other particular properties according to optical properties, e.g. colour of granular material, e.g. ore particles, grain by changing or intensifying the optical properties prior to scanning, e.g. by inducing fluorescence under UV or x-radiation, subjecting the material to a chemical reaction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/363—Sorting apparatus characterised by the means used for distribution by means of air
    • B07C5/365—Sorting apparatus characterised by the means used for distribution by means of air using a single separation means
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
    • A23L25/00—Food consisting mainly of nutmeat or seeds; Preparation or treatment thereof
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
    • A23L5/00—Preparation or treatment of foods or foodstuffs, in general; Food or foodstuffs obtained thereby; Materials therefor
    • A23L5/30—Physical treatment, e.g. electrical or magnetic means, wave energy or irradiation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0098—Plants or trees
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N2021/4735—Solid samples, e.g. paper, glass

Definitions

  • the present disclosure relates to a method and system for classification of an edible grain seed using electromagnetic radiation. While an application of the present disclosure is in the classification of edible seeds, it could equally well be applied to the classification of any foodstuff for human and/or animal consumption. Background of Invention Aflatoxin is a known carcinogen globally affecting over 25% of crops mainly tree nuts, peanuts, maize, cottonseed. Over 4 billion people worldwide are exposed to dietary aflatoxins. It is estimated that between 5-28% of liver cancers in humans could be attributed to aflatoxins.
  • LC liquid chromatography
  • UV fluorescence UV fluorescence
  • aflatoxin can be detected around a 435 nm wavelength when exposed to UV light at 365 nm.
  • the whiter the substrate the easier the recognition of aflatoxin from the image.
  • the coloured skin of certain foodstuffs, such as the brown skin of almonds cancels the effect of fluorescence from aflatoxin and impedes the effective use of UV light for detection of aflatoxin.
  • the brown surface of almonds produces fluorescence response at high spectrum levels from any applied light source with a large shift of frequency. Therefore, what is needed is a rapid, non-destructive, reliable, and accurate detection and measurement of aflatoxin for the almond industry.
  • the present disclosure in one aspect sets out a method for classifying a foodstuff.
  • the foodstuff may be an edible seed (including any grain, nut or legume).
  • the method may include illuminating the edible seed with at least one wavelength of electromagnetic radiation, the at least one wavelength of electromagnetic radiation is partially reflected by each seed including specific signals from aflatoxin if it is present, detecting the reflected signal to provide a detected aflatoxin signal, comparing the detected aflatoxin signal with predetermined reflectance levels from known concentrations of aflatoxin to provide the first accurate measurement of aflatoxin concentrations, and classifying the edible seed aflatoxin concentration.
  • the present disclosure in another aspect sets out a scanning device for classifying a foodstuff.
  • the foodstuff may be an edible seed, including any grain, nut or legume.
  • the scanning device may include a reservoir configured to hold at least one edible seed, a chute, having at least one electromagnetic radiation source to illuminate the seed with at least three wavelengths of electromagnetic radiation, the at least three wavelengths of electromagnetic radiation causing a reflection signal of at least one type of aflatoxin, a camera configured to detect the reflected light, a filter to remove extraneous reflections, and to provide the aflatoxin signal; and a microprocessor configured to: compare the detected aflatoxin signal with predetermined signals of known concentration of the at least one type of aflatoxin to provide a first calibrated measurement of aflatoxin concentration, and classify the edible seed relative to the first measured aflatoxin concentration, accurate to +/- 0.16 ⁇ g.
  • Foodstuff for human consumption and/or animal consumption must be kept dry and free from mould to reduce the risks associated with contamination. It is not always possible to eliminate the growth of mould on foodstuff and, accordingly, it would be beneficial to have a method of classifying the level of contamination of a foodstuff by a mould, for example Aspergillus sp.
  • Foodstuff for human consumption that may be contaminated by mould under inappropriate storage conditions includes edible seeds, cereals, fruits, and vegetables.
  • Foodstuff for animal consumption that may be contaminated by mould under inappropriate storage conditions include hay, fodder, feed grains, animal feed pellets, animal feed crumbles, and animal feed mixes.
  • the present disclosure relates to a method for detecting an aflatoxin on a seed.
  • the method includes sorting a plurality of seeds (including any grains, nuts or legumes) in a single file array; capturing a plurality of near or shortwave infrared images of each seed; comparing wavelengths from the captured image with wavelengths indicative of an aflatoxin presence at a predetermined concentration; and ejecting from the plurality of seeds those seeds that have an aflatoxin concentration greater than the predetermined concentration as indicated by the wavelengths from the captured images.
  • the captured images are pre-processed using the Savitzky-Golay 2nd (SG-2nd) derivative, to reveal some major differences in the reflectance intensities of the control and contaminated samples of almond kernels around 960 nm, 980 nm, 1050 nm, 1110 nm, 1150 nm, 1210 nm, 1250 nm, 1340 nm, 1390 nm, 1450 nm, and 1680 nm (feature wavelengths).
  • a competitive adaptive reweighted sampling (CARS) algorithm is used to select the feature wavelengths, removing the remaining redundant wavelengths from the model, for extraction of useful information in the shortest possible time, allowing for rapid quantification of AFB1 at commercial scale.
  • CARS competitive adaptive reweighted sampling
  • reflectance of one or more control seed is measured at the feature wavelengths and compared to reflectance of the seed to be tested for contamination at those same feature wavelengths.
  • this embodiment enables determination of the concentration (or level) of aflatoxin in the seed.
  • multiple linear regression (MLR) models are used for quantification of aflatoxin concentration, to simplify the prediction process and to improve calculation speed.
  • a formula used for the calculations may be: where Y represents the aflatoxin concentration ( ⁇ g/g); ⁇ 0 is the intercept; ⁇ i and ⁇ ij are the linear and interactive coefficients; Xi, Xj are the reflectance values of the feature wavelengths and ⁇ is error; k is the number of feature wavelengths used.
  • the present disclosure relates to a system for determining when a seed has an unsafe concentration of aflatoxin.
  • the system includes a seed reservoir; a chute from the reservoir to a rotating glass disc; a plurality of light sources configured to emit a suppressed visible light upon each gain seed; and a plurality of hyperspectral or multi-spectral cameras configured to each capture a plurality of spectral images of each seed.
  • the system also includes a processor configured to align and segment a spectral cube, determine an average reflectance for each spectral image, compare the average reflectance with a predetermined reflectance value indicative of a presence of an aflatoxin concentration designated to fail a predetermined health standard, and send instructions to a diverter to separate a seed determined to fail the health standard.
  • filter wheels may be used to position a selected filter, or combination of filters, in the imaging path quickly and accurately. This may attenuate the light intensity or prevent unwanted spectral wavelengths from contaminating the image. Because filter wheels move, it may be preferable to use a multi-spectral camera. What is needed is a low cost and high speed multi-spectral camera.
  • the present disclosure relates to a multi-spectral imaging system having one or more camera, one or more lens, and one or more filter. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
  • the word “comprising” and its derivatives including “comprises” and “comprise” include each of the stated integers but does not exclude the inclusion of one or more further integers. It will be appreciated that reference herein to “preferred” or “preferably” is intended as exemplary only. The claims as filed and attached with this specification are hereby incorporated by reference into the text of the present description.
  • Fig.1 is a top view of a scanning device in accordance with a preferred embodiment of the present disclosure.
  • Fig.2 is a front view of the scanning device of Fig.1.
  • Fig.3 is another side view of the scanning device of Fig.1.
  • Fig.4 is a bottom view of the scanning device of Fig.1.
  • Fig.5 is a perspective view of the scanning device of Fig.1.
  • Fig.6 is an image comparing spectral radiant flux with wavelength.
  • Fig.7 is a chart demonstrating the feature wavelength selection by CARS for AFB1 prediction.
  • Fig.8 (a) is a chart demonstrating mean reflectance of spectra obtained from control and aflatoxin infected almond kernels.
  • Fig.8 (b) is a chart demonstrating mean reflectance of SG-2nd derivative pre- processed spectra obtained from control and aflatoxin infected almond kernels.
  • Fig.9 is a multi-spectral system with a spinning wheel.
  • Fig.10 is a NIR light system.
  • Scanning device 10 includes a reservoir 12, a chute 14, a near infrared (NIR) camera detector 16, and a microprocessor.
  • Reservoir 12 is configured to hold at least one edible seed.
  • Scanning device 10 has at least one electromagnetic radiation source 18.
  • the electromagnetic radiation source 18 illuminates the edible seed with at least one wavelength of electromagnetic radiation.
  • the reflectance can be measured and after calibration the detection of at least one type of aflatoxin can be accurately measured.
  • An NIR camera 16 is configured to capture any reflected light.
  • the microprocessor is configured to compare the various wavelengths of this reflectance and accurately measure any levels of aflatoxin that may be present by comparison with previous calibrations from accurately laboratory dosed specimens.
  • a signal detector 16 is a near infrared (NIR) hyperspectral or multi-spectral camera.
  • NIR near infrared
  • NIR near infrared
  • hyperspectral imaging acquires images with hundreds of continuous wave bands usually by the application of a spectrograph and a sensitive area detector whereas for multi-spectral imaging a sensitive area detector is usually paired with a or series of specific waveband filters or a waveband tunable light source. The result of both systems is called a data cube.
  • This data cube is composed of a M x N x W matrix, where M and N are the position data and W is the waveband value.
  • Scanning device 10 further includes a visible light source 20 and a visible light detector 22.
  • Visible light source 20 illuminates the edible seed and visible light detector 22 detects an image of the edible seed.
  • visible light source 20 is a halogen light source.
  • the microprocessor is configured to detect a blemish on a surface of the edible seed as shown in the image, compare the blemish to a predetermined blemish signal of a known concentration of the aflatoxin to provide a second estimated aflatoxin concentration, and classify the edible seed relative to the second estimated aflatoxin concentration.
  • a multiple linear regression (MLR) model may be used for quantification of aflatoxin concentration, to simplify the prediction process and to improve calculation speed.
  • a formula used for the calculations may be: where Y represents the aflatoxin concentration ( ⁇ g/g); ⁇ 0 is the intercept; ⁇ i and ⁇ ij are the linear and interactive coefficients; Xi, Xj are the reflectance values of the feature wavelengths and ⁇ is error; k is the number of feature wavelengths used.
  • the image has a dimension approximately four times the width of the edible seed.
  • visible light detector 22 is an RGB light camera.
  • scanning device 10 includes a NIR spectrophotometer. RGB cameras are used on each side of the glass plate and NIR cameras, from both sides also.
  • Aflatoxin B1 Aflatoxin B1
  • Aflatoxin B2 Aflatoxin B2
  • AFG1 Aflatoxin G1
  • AFG2 Aflatoxin G2
  • Detectable aflatoxins can be produced by Aspergillus flavus or Aspergillus parasiticus.
  • the edible seed may be any grain, nut or legume, for example only, an almond, a Brazil nut, a candlenut, a cashew, a Chilean hazelnut, a hazelnut, a macadamia, a peanut, a pecan, a pine nut, a pistachio, a walnut, maize, cottonseed or any other cereal, oil seed, pulse, peanut or tree nut.
  • the scanning device 10 and the reservoir 12 may be configurable to accommodate any foodstuff suitable for human and/or animal consumption.
  • a plurality of images of each edible seed is compiled, preferably using a virtual cube, to provide a composite 3D representation of the edible seed.
  • Artificial intelligence i.e., deep learning, including forward selection and backward elimination, stepwise selection, etc., is employed to discern all fault parameters of the edible seed. Where more than one fault is present on an edible seed, the suitability determination is determined by what is least desirable for the final consumer to experience/taste - that is fault parameters are ranked.
  • the edible seeds can be sorted to achieve desired specifications for the edible seeds, e.g., edible seeds can be categorised for human or animal consumption, for meal, crushed nuts, or nut milk production. The data produced from this analyser determination informs down the line managers in sorting, processing and marketing.
  • the system can be linked to digital sorting machines to enhance performance to be focused only on customer specifications.
  • the scanning device can be connected to a network or the internet for online management and/or reporting.
  • the scanning device may include at least one glass plate for the edible seed to be supported and/or conveyed across.
  • the scanning device may include a mechanism to clean the glass plate.
  • the glass plate may be electrically charged to repel dust particles.
  • a dust particle may include a fibre, particulate organic material, or the like. Such particulate organic material may originate from the edible seed. Referring to Figs.1 to 5, a preferred method for classifying an edible seed is described below. The method includes illuminating the edible seed with a number of wavelengths of NIR electromagnetic radiation.
  • the reflectance of electromagnetic radiation from the seed is then analysed and compared to the reflectance generated by seeds dosed at known rates of aflatoxin in a laboratory.
  • the calibration used is preferably measuring Aflatoxin B1 to +/- 0.16 ⁇ g/g.
  • the wavelength of electromagnetic radiation is near infrared radiation. The specific wavelength used will have an impact on the accuracy and speed of the results, allowing for rapid quantification of AFB1 at commercial scale.
  • the feature wavelengths may be determined by pre-processing captured images using the Savitzky-Golay 2nd (SG-2nd) derivative, to reveal any major differences in the reflectance intensities of the control and contaminated samples of edible seed.
  • the feature wavelengths may be determined by a competitive adaptive reweighted sampling (CARS) algorithm, removing the remaining redundant wavelengths from the model, for extraction of useful information in the shortest possible time.
  • the aflatoxin may be produced by Aspergillus sp. More particularly, the aflatoxin may be produced by Aspergillus flavus Aspergillus parasiticus.
  • the detectable aflatoxin is at least one of aflatoxin B1, aflatoxin B2, aflatoxin G1, or aflatoxin G2.
  • Fig.6 shows a full spectrum, with visible NIR range as indicated.
  • Hyperspectral imaging is a combination of both spatial imaging and spectroscopy (spectral).
  • the hyperspectral images are three–dimensional arrays (m ⁇ n ⁇ ⁇ ), where m and n are the spatial axes and ⁇ is the spectral information.
  • Hyperspectral cameras are used with broadband halogen lights that produce the light source in full spectrum. Objects subjected to high values of light will produce light noise at higher range of the spectrum. This noise carries colour information that obscure chemical information.
  • Filter wheels may be used to position a selected filter, or combination of filters, in the imaging path quickly and accurately. This may attenuate the light intensity or prevent unwanted spectral wavelengths from contaminating the image.
  • a hyperspectral camera cannot be used as the speed of the filter wheel means that the camera is required to scan at a speed of over 20 data cubes per second. At this speed, hyperspectral cameras will show a deformed image as the object rotates in front of the camera and even after correcting the pixel position by curvature the mean spectrum of the product will suffer distortions. In such cases, a multi-spectral camera may be used.
  • SpectroCam from Ocean Insight can only take two to four data cubes per second.
  • Other commercial grade equipment such as the MS-IR from Telops has a high rate of frames per second and can accommodate up to 8 filters, but the cost of deployment can be up to twice the cost of a commercially available area scan hyperspectral camera.
  • the present disclosure includes a high speed multispectral system that is capable of replacing a hyperspectral camera in a production line and can be designed and prototyped with lower cost than an equivalent hyperspectral or commercially available multispectral system.
  • Fig.7 shows the feature wavelength selection for AFB1.
  • the line start starts at above 200 sampled variables 22 indicates the selected wavelengths, which reduces as the sampling increases.
  • the line that starts at about 50 sampled variables 24 shows the variation of root mean square error of cross-validation (RMSECV) values with the number of sampling.
  • RMSECV root mean square error of cross-validation
  • RMSECV values 24 were first reduced moderately and then gradually increased with the elimination of feature wavelengths.
  • the optimum wavelengths were chosen based on the minimum RMSECV values 24.
  • the RMSECV value 24 was lowest at the 15th sampling and the corresponding number of feature wavelengths was 58.
  • Fig.8 (a) shows the mean reflectance of spectra obtained from control and contaminated samples of almond kernels.
  • Fig.8 (b) shows the mean reflectance of Savitzky-Golay 2nd derivative pre- processed spectra obtained from control and contaminated samples of almond kernels.
  • FIG.9 is a multi-spectral camera system with a filter wheel 34.
  • Filter wheel 34 may include a wheel and filters. In a preferred embodiment, there may be 8 filters. These may be NIR filters, of 32mm. Filter wheel 34 may be rotated by a motor 28.
  • Motor 28 may be a SC040A servomotor as it has a PID controller for the motor RPM and can be precisely positioned to capture the required images.
  • SC040A, servomotor, and PID would be understood by a person skilled in the art.
  • Filter wheel 34 may have a permanent rotation at specific RPM with high accuracy, and a position or absolute encoder 26 which measures the current location of the filter wheel 34 and overrides any microcontroller (MCU) in camera to trigger filter wheel 34 when the desired filter is in position, and two pure NIR lights 32 to provide the correct illumination.
  • MCU microcontroller
  • the angle region in which each filter will be on view, encoder pulses and minimum integration time for the camera may be calculated by: where r is the radius from the filter location to motor 28, ⁇ F is the sum of the giving space at each side of the filter, Encoder pulses are the number of division available for each revolution of encoder 26, and RPS is the revolution per second of the system.
  • the pseudocode for triggering of the multispectral camera may be in according with the algorithm: The first section calculates the delay time of an incoming trigger signal and stores it in a matrix of delays. The second section executes the delays stored in the matrix of delays depending on the current time and filter wheel 34 position. The pseudocode is designed to always start from first filter position and produce 8 images per cycle.
  • the pseudocode may be written in C for microcontrollers Microchip ATmega328P running at 16 MHZ.
  • the integration between the photoelectric switch and the trigger delay MCU may be done with a 4n25 optocoupler for DC systems.
  • the use of the optocoupler is to reduce the possibility of damage to the camera by short circuits in the system.
  • the multi-spectral camera system will work in the range of 900 nm to 1700 nm, scan at a speed of at least 20 data cubes per second, synchronise with other cameras in the system, scan a range of 1 to 8, for example, 6 wavebands, enable identification of the data in each waveband, order the data captured, and allow the correct illumination of the area to be scanned.
  • Fig.10 is a NIR light system.
  • Each NIR light 38 may be composed of a Quartz tungsten halogen light 40, a Quartz clear lens 42 and a high pass filter 46.
  • Halogen light 40 produces almost homogeneous light in the range between 400-2500nm. This light source however emits radiation and heat.
  • Quartz clear lens 42 is placed in front of the lamp to reduce the heat against high pass filter 46. Heat may also be reduced by a cooling fan 44. Quartz clear lens 42 may be placed at 1mm from the highest element of filter wheel 34.
  • high pass filter 46 removes the visible light components. High pass filter 46 may be located at a minimum radius of 100mm.
  • the aflatoxin infested almonds were dried under natural air for drying of the methanol and water solution completely. Images of single almonds were scanned using a hyperspectral camera in the wavelength range of 900-1700 nm, one by one. Using 224 number of images of one almond at various wavelengths, the hyperspectral camera synthesized a hyperspectral image cube. The average spectra of each image were extracted after background removal and library of 3000 spectra was constructed. After capturing the images, the almond samples were destroyed, and aflatoxin content was verified using High Performance Liquid Chromatology (HPLC). (b) Moisture content analysis Nonpareil variety of healthy almonds kernels were selected as samples which have good appearance and uniform size. The sample bulk was divided into four groups.
  • the PLSR model may follow the equation: where X is an n x m matrix of predictors, indicating n is the number of samples and m is the number of wavelengths; Y is an n x 1 matrix of response variable; T is the score matrix of wavelengths, P is the m x k matrix of X loadings and Q is (1 x k) matrix of Y loadings (k is the number of latent variables); Y is the reference data (n x 1) to be predicted from X.
  • E and F stand for random errors in X and Y, respectively, while W is PLS weights, and Wk is the m x k matrix of X weights.
  • the association between spectra matrix (X) and quality attribute (Y) may be predicted using equations: where ⁇ is m x by the PLSR model equations. ⁇ is the predicted value of the response of interest.
  • the ideal number of latent variables is decided by cross validation when the root mean square error of cross-validation (RMSECV) reaches a minimum.
  • the RMSECV is preferably calculated by the equations: (e)
  • NIR near infrared
  • Two NIR multispectral camera with specific number of frequencies to capture a series of spectral images to assemble a spectral cube A rotating glass system presents single almonds to the capture system, one at a time. Almonds flowing in single file trigger a light sensor, the advance spectral camera system acquires multiple times for each almond and converted to a series of short pulses for each wavelength required to capture. The cube capture is aligned and segmented to extract the corresponded frequencies for the respective almond in the frame. The average reflectance for each spectrum is calculated. The average spectrums of the wavelengths used (or displayed) to calculate the moisture, FFA, PV and aflatoxin content of a single almond.
  • the system and method described herein has many benefits and advantages.
  • features of one or more embodiments described herein can generate an almond colour index, and even discern one or more of scratches, chips, stains, embedded shells, insect damage, mould, and other characteristics on almonds that could affect the quality of the almond.
  • the features described with respect to one embodiment may be applied to other embodiments or combined with or interchanged with the features of other embodiments, as appropriate, without departing from the scope of the present invention.
  • Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
  • U.S. Patent No.10,021,369 is hereby incorporated by reference herein in its entirety.
  • Aflatoxins are poisonous carcinogens and mutagens that are produced by certain moulds (Aspergillus flavus and Aspergillus parasiticus) which grow in soil, decaying vegetation, hay, and grains.
  • Rancidity is the process of complete or incomplete oxidation or hydrolysis of moisture and/or oils when exposed to air, light, or humidity, resulting in unpleasant taste and odour. Pathways for rancidification include hydrolytic rancidity and oxidative rancidity.
  • Free fatty acids (FFA) are the by-product of hydrolytic rancidity, and it is one of the parameters used for measuring rancidity in almonds.
  • Peroxide value (PV) are the by-products of oxidative rancidity.
  • NIR light is reflective light; it bounces off objects much like visible light.
  • NIR light is typically defined as light in the 0.9 – 1.7 ⁇ m wavelength range but can also be classified from 0.7 – 2.5 ⁇ m. NIR images are not in colour, making objects composition easily recognisable.
  • Spectral cube is a group of spectral images in which each layer is composed of an image taken in a specific wavelength. Like an RGB image where each layer of the three images represent the wavelengths of red, green, blue. Spectral cubes are composed of higher number of layers.
  • Spectra is the representation of the reflectance values of an image at various wavelengths.
  • Multispectral camera captures image data within few specific wavelength ranges across the electromagnetic spectrum.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Immunology (AREA)
  • General Physics & Mathematics (AREA)
  • Pathology (AREA)
  • Food Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Botany (AREA)
  • Wood Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Nutrition Science (AREA)
  • Polymers & Plastics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé pour la détection d'une aflatoxine sur une graine, qui consiste à déterminer des longueurs d'onde auxquelles il existe la plus grande différence dans les intensités de réflectance de graines témoin et contaminée, et comparer des intensités de réflectance provenant de l'image capturée avec des intensités de réflectance indicatives de la présence d'une aflatoxine à une concentration prédéterminée. Des graines sont éjectées, qui possèdent une concentration en aflatoxine supérieure à la concentration prédéterminée comme indiqué par les intensités de réflectance provenant des images capturées. Des exemples de graines comprennent un fruit à coque ou une légumineuse.
PCT/AU2023/050805 2022-08-24 2023-08-23 Procédé amélioré pour classification de semence comestible et dispositif de balayage s'y rapportant WO2024040292A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2022902420A AU2022902420A0 (en) 2022-08-24 Improved method for classification of an edible seed and a scanning device therefor
AU2022902420 2022-08-24

Publications (1)

Publication Number Publication Date
WO2024040292A1 true WO2024040292A1 (fr) 2024-02-29

Family

ID=90011973

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2023/050805 WO2024040292A1 (fr) 2022-08-24 2023-08-23 Procédé amélioré pour classification de semence comestible et dispositif de balayage s'y rapportant

Country Status (1)

Country Link
WO (1) WO2024040292A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120061586A1 (en) * 2010-09-10 2012-03-15 Haibo Yao Method and detection system for detection of aflatoxin in corn with fluorescence spectra
JP2012177607A (ja) * 2011-02-25 2012-09-13 National Agriculture & Food Research Organization アフラトキシン検知方法、アフラトキシン検知装置、および、プログラム
EP1332354B1 (fr) * 2000-10-30 2013-08-21 Monsanto Technology LLC Procede et dispositif pour l'analyse de produits agricoles
US20190293620A1 (en) * 2018-03-20 2019-09-26 SafetySpect, Inc. Apparatus and method for multimode analytical sensing of items such as food
WO2021134110A1 (fr) * 2019-12-29 2021-07-08 Surenut Pty Ltd Procédé de classification d'une semence comestible et dispositif de balayage associé
CN114778457A (zh) * 2022-03-04 2022-07-22 北京市农林科学院智能装备技术研究中心 一种谷物中黄曲霉毒素b1含量检测方法及装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1332354B1 (fr) * 2000-10-30 2013-08-21 Monsanto Technology LLC Procede et dispositif pour l'analyse de produits agricoles
US20120061586A1 (en) * 2010-09-10 2012-03-15 Haibo Yao Method and detection system for detection of aflatoxin in corn with fluorescence spectra
JP2012177607A (ja) * 2011-02-25 2012-09-13 National Agriculture & Food Research Organization アフラトキシン検知方法、アフラトキシン検知装置、および、プログラム
US20190293620A1 (en) * 2018-03-20 2019-09-26 SafetySpect, Inc. Apparatus and method for multimode analytical sensing of items such as food
WO2021134110A1 (fr) * 2019-12-29 2021-07-08 Surenut Pty Ltd Procédé de classification d'une semence comestible et dispositif de balayage associé
CN114778457A (zh) * 2022-03-04 2022-07-22 北京市农林科学院智能装备技术研究中心 一种谷物中黄曲霉毒素b1含量检测方法及装置

Similar Documents

Publication Publication Date Title
Lu et al. Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress
Aviara et al. Potential application of hyperspectral imaging in food grain quality inspection, evaluation and control during bulk storage
Femenias et al. Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review
US20220412884A1 (en) A method for classification of an edible seed and a scanning device therefor
Wu et al. Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques
Mahendran et al. Application of computer vision technique on sorting and grading of fruits and vegetables
Singh et al. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging
Saldaña et al. Computer vision applied to the inspection and quality control of fruits and vegetables
Mehl et al. Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis
Moscetti et al. Hazelnut quality sorting using high dynamic range short-wave infrared hyperspectral imaging
Lunadei et al. Image-based screening for the identification of bright greenish yellow fluorescence on pistachio nuts and cashews
EP3999845B1 (fr) Moyens et procédés d'évaluation de fruits et légumes
Shah et al. Imaging techniques for the detection of stored product pests
Ma et al. Spectroscopy and computer vision techniques for noninvasive analysis of legumes: A review
Lee et al. Application of nondestructive measurement to improve soybean quality by near infrared reflectance spectroscopy
WO2023272338A1 (fr) Procédé pour la classification d'une semence comestible et dispositif de balayage pour celui-ci
Tunny et al. Hyperspectral imaging techniques for detection of foreign materials from fresh-cut vegetables
Mo et al. Discrimination methods for biological contaminants in fresh-cut lettuce based on VNIR and NIR hyperspectral imaging
Alamar et al. Hyperspectral Imaging Techniques for Quality Assessment in Fresh Horticultural Produce and Prospects for Measurement of Mechanical Damage
Jayas et al. Grain quality evaluation by computer vision
Yu et al. Non-destructive detection and classification of in-shell insect-infested almonds based on multispectral imaging technology
Femenias et al. Hyperspectral Imaging
WO2024040292A1 (fr) Procédé amélioré pour classification de semence comestible et dispositif de balayage s'y rapportant
Singh et al. Optical sensors and online spectroscopy for automated quality and safety inspection of food products
Wang et al. Bruise detection of apples using hyperspectral imaging

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23855840

Country of ref document: EP

Kind code of ref document: A1